

#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# You may need to add --master_addr $MASTER_ADDR --master_port $MASTER_PORT if using a custom addr:port
#
# You can also use the rdzv API: --rdzv_endpoint $MASTER_ADDR:$MASTER_PORT --rdzv_backend c10d
#
# use torch.distributed.launch instead of torch.distributed.run for torch < 1.9
#
# If you get a hanging in `barrier` calls you have some network issues, you may try to debug this with:
#
# NCCL_DEBUG=INFO python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# which should tell you what's going on behind the scenes.
#
#
# This script can be run via `srun` in the SLURM environment as well. Here is a SLURM script that
# runs on 2 nodes of 4 gpus per node:
#
# #SBATCH --job-name=test-nodes        # name
# #SBATCH --nodes=2                    # nodes
# #SBATCH --ntasks-per-node=1          # crucial - only 1 task per dist per node!
# #SBATCH --cpus-per-task=10           # number of cores per tasks
# #SBATCH --gres=gpu:4                 # number of gpus
# #SBATCH --time 0:05:00               # maximum execution time (HH:MM:SS)
# #SBATCH --output=%x-%j.out           # output file name
#
# GPUS_PER_NODE=4
# MASTER_ADDR=$(scontrol show hostnames $SLURM_JOB_NODELIST | head -n 1)
# MASTER_PORT=6000
#
# srun --jobid $SLURM_JOBID bash -c 'python -m torch.distributed.run \
# --nproc_per_node $GPUS_PER_NODE --nnodes $SLURM_NNODES --node_rank $SLURM_PROCID \
# --master_addr $MASTER_ADDR --master_port $MASTER_PORT \
# torch-distributed-gpu-test.py'
#

import fcntl
import os
import socket

import torch
import torch.distributed as dist






def        snake_case__		(    *lowerCamelCase_		):
							with open(lowerCamelCase_     ,				'''r'''		) as fh:
														fcntl.flock(lowerCamelCase_     ,				fcntl.LOCK_EX		)
														try:
																					print(*lowerCamelCase_		)
														finally:
																					fcntl.flock(lowerCamelCase_     ,				fcntl.LOCK_UN		)


lowercase			:				Optional[Any]   =     int(os.environ["LOCAL_RANK"])
torch.cuda.set_device(local_rank)
lowercase			:				Dict   =     torch.device("cuda", local_rank)
lowercase			:				Optional[Any]   =     socket.gethostname()

lowercase			:				Optional[int]   =     F"[{hostname}-{local_rank}]"

try:
		# test distributed
		dist.init_process_group("nccl")
		dist.all_reduce(torch.ones(1).to(device), op=dist.ReduceOp.SUM)
		dist.barrier()

		# test cuda is available and can allocate memory
		torch.cuda.is_available()
		torch.ones(1).cuda(local_rank)

		# global rank
		lowercase			:				Any   =     dist.get_rank()
		lowercase			:				Optional[Any]   =     dist.get_world_size()

		printflock(F"{gpu} is OK (global rank: {rank}/{world_size})")

		dist.barrier()
		if rank == 0:
				printflock(F"pt={torch.__version__}, cuda={torch.version.cuda}, nccl={torch.cuda.nccl.version()}")

except Exception:
		printflock(F"{gpu} is broken")
		raise









import string
from math import logaa






def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_		):
							A      :					List[str]      		=					document.translate(
							    str.maketrans(''''''     ,				''''''     ,				string.punctuation		)		).replace('''\n'''     ,				''''''		)
							A      :					Optional[int]      		=					document_without_punctuation.split(''' '''		)  # word tokenization
							return len([word for word in tokenize_document if word.lower() == term.lower()]		)






def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_		):
							A      :					Dict      		=					corpus.lower().translate(
							    str.maketrans(''''''     ,				''''''     ,				string.punctuation		)		)  # strip all punctuation and replace it with ''
							A      :					Dict      		=					corpus_without_punctuation.split('''\n'''		)
							A      :					List[Any]      		=					term.lower()
							return (len([doc for doc in docs if term in doc]		), len(lowerCamelCase_		))






def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_=False		):
							if smoothing:
														if n == 0:
																					raise ValueError('''log10(0) is undefined.'''		)
														return round(1 + logaa(n / (1 + df)		)     ,				3		)

							if df == 0:
														raise ZeroDivisionError('''df must be > 0'''		)
							elif n == 0:
														raise ValueError('''log10(0) is undefined.'''		)
							return round(logaa(n / df		)     ,				3		)






def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_		):
							return round(tf * idf     ,				3		)






